The association between dietary diabetic risk reduction score with anthropometric and body composition variables in overweight and obese women: a cross-sectional study

Dietary diabetes risk reduction score (DDRRs) is inversely associated with a lower risk of type 2 diabetes. Given the importance of the association between body fat and insulin resistance and the effect of diet on these parameters, this study aimed to investigate the association between DDRRS and body composition parameters, including the visceral adiposity index (VAI), lipid accumulation product (LAP), and skeletal muscle mass (SMM). This study was conducted on 291 overweight and obese women aged 18–48 years old recruited from 20 Tehran Health Centers in 2018. The anthropometric indices, biochemical parameters, and body composition were measured. A semi-quantitative food frequency questionnaire (FFQ) was used to calculate DDRRs. Linear regression analysis was used to examine the association between DDRRs and body composition indicators. The mean (SD) age of participants was 36.67 (9.10) years. After adjustment for potential confounders, VAI (β = 0.27, 95% CI = − 0.73, 1.27, Ptrend = 0.052), LAP (β = 8.14, 95% CI = − 10.54, 26.82, Ptrend = 0.069), TF (β = − 1.41, 95% CI = 11.45, 17.30, Ptrend = 0.027), trunk fat percent (TF%) (β = − 21.55, 95% CI = − 44.51, 1.61, Ptrend = 0.074), body fat mass (BFM) (β = − 3.26, 95% CI = − 6.08, − 0.44, Ptrend = 0.026), visceral fat area (VFA) (β = − 45.75, 95% CI = − 86.10, − 5.41, Ptrend = 0.026), waist-to-hip ratio (WHtR) (β = − 0.014, 95% CI = − 0.031, 0.004, Ptrend = 0.066), visceral fat level (VFL) (β = − 0.38, 95% CI = − 5.89, 5.12, Ptrend = 0.064), fat mass index (FMI) (β = − 1.15, 95% CI = − 2.28, − 0.02, Ptrend = 0.048) decreased significantly over tertiles of DDRRs, and also there was no significant association between SMM and DDRRs tertiles (β = − 0.57, 95% CI = − 1.69, 0.53, Ptrend = 0.322). The findings of this study demonstrated that participants with higher adherence to the DDRRs had lower VAI (β = 0.78 vs 0.27) and LAP (β = 20.73 vs 8.14). However, there was no significant association between DDRRs and VAI, LAP and SMM, which are mentioned as the primary outcomes. Future studies with larger sample of both genders are needed to investigate our findings.

Obesity which is increasing globally is a major risk factor for a wide range of chronic diseases 1 . The World Health Organization (WHO) has defined overweight and obesity as abnormal or excessive fat accumulation, a body mass index (BMI) ≥ 25 kg/m 2 and ≥ 30 kg/m 2 , respectively 2 . According to the latest report by the WHO, over 1.9 billion adults were overweight, and of these, 650 million were obese in 2016 3 . Also, in 2016, the prevalence of overweight and obesity was 60.9% and 25.5% in Iran, respectively 4,5 . The results of several studies showed a higher prevalence of obesity in women. Furthermore, females with a higher BMI are at increased risk for breast cancer, atherosclerotic cardiovascular disease, hypertension, dyslipidemia, type 2 diabetes (T2D), and endocrine disorders [6][7][8] . Obesity is commonly defined using BMI, while the evidence shows that this indicator is not a strong predictor of medical risks. Given the complicated function of adipose tissue, the distribution of lipids in different anatomic regions is more important for predicting diseases 9 . LAP and VAI, novel insulin resistance biomarkers are measured through anthropometric indices and metabolic parameters. LAP is calculated from waist circumference (WC) and fasting concentration of TGs, and VAI is calculated using the combination of BMI, WC, TGs, and high-density cholesterol (HDL) 10,11 . A systematic review and meta-analysis showed that, LAP is an inexpensive method to evaluate the risk of all-cause mortality, and hypertension. Also, it is an accurate indicator for diagnosing and evaluating diabetes, which can perform better than anthropometric indicators in this field 12 . Furthermore, another systematic review study reported a strong association between diabetes risk and LAP 13 .
The evidence has shown that lifestyle changes with diet modification are necessary to prevent obesity and its health outcomes 14,15 . Given foods and nutrients are consumed together, the dietary pattern approach enables researchers to examine the whole diet 16 . DDRRs was created by Rhee et al. to indicate a higher consumption of coffee, nuts, cereal fibre, and a high ratio of polyunsaturated fats (PUFA)/saturated fats (SFA), and a lower intake of high glycemic index (GI) foods, sugar-sweetened beverages (SSB), red and processed meats, and trans fatty acids 17 . While DDRRs includes lower GI foods and higher cereal fibre intake, which are components of a healthy diet and reduce the incidence of overweight and obesity, no previous study has examined the association between DDRRs with overweight and obesity in Iranian adults 18 .
(HC) were measured for each participant by a trained dietitian. Weight was measured using BIA, and height was measured with an accuracy of 0.1 cm using a Seca scale 206 while participants were in a standing position without shoes. WC was measured in the narrowest area of the waist and on bare skin without any pressure on the body, at the end of the natural exhalation, using a non-elastic tape with an accuracy of 0.5 cm. Using a strapless tape on the most prominent part that was marked, we measured the HC with an accuracy of 0.5 cm. To measure the arm circumference (AC), it was kept in a contracted position in line with the body and the elbow was bent 90° upwards, then its most prominent part was measured using a caliper. WHtR was calculated as WC (cm) divided by height (cm). All measurements were taken in morning before breakfast and were performed by one person to reduce the measurement errors.
LAP and VAI equations. VAI was calculated using sex-specific formulas, where both TGs and HDL levels are expressed in mmol/L 10 .
LAP was calculated as (WC/65) × TG in men, and (WC/58) × TG in women 26 . Blood sampling. Participants in this study were referred to the Nutrition and Biochemistry Laboratory of the School of Nutritional and Dietetics at Tehran University of medical sciences. After fasting for 10-12 h, 12 cm 3 of venous blood samples were taken. Blood samples were collected in two tubes (one tube contained EDTA anticoagulant while another tube lacked this substance). The blood was centrifuged for 15 min at 3000 rpm, and the remaining blood was washed three times with 0.9% NaCl solution. Following serum separation, it was kept at − 80 °C for laboratory assessments.
Blood pressure assessment and laboratory measurements. Before the blood pressure measurement, participants were asked about their intake of coffee and tea, as well as recent physical activity. Blood pressure was measured using a standard mercury sphygmomanometer, with appropriate cuffs, after 15 min of resting. A mean of two measurements was calculated for each individual 27 . The serum fasting glucose concentration was measured using an enzymatic colourimetric method with the glucose oxidase technique. The insulin level was assessed using the enzyme-linked immunosorbent assay (ELISA) kit (Human insulin ELISA kit, DRG Pharmaceuticals, GmbH, Germany). Serum TG level was measured using the glycerol-3-phosphate oxidase phenol 4-amino antipyrine peroxidase (GPO-PAP) method. ALT and AST were measured based on the standard protocols. Total cholesterol (CHOL) levels were assessed based on the enzymatic endpoint method. Low-density lipoprotein-cholesterol (LDL-C) and HDLC were measured using direct enzymatic clearance. All evaluations were performed using Pars Azmoon laboratory kits (Test Pars Inc, Tehran, Iran).
HOMA and ISQUICKI calculations. Insulin resistance was measured using HOMA. The HOMA was Statistical analysis. Statistical analysis was performed using the IBM SPSS software version 25.0 (SPSS, Chicago, IL, USA) and P-value < 0.05 was considered statistically significant and 0.05, 0.06, and 0.07 were considered marginally significant. Continuous and categorical variables were reported as means and standard deviations (SD), and number and percentage, respectively. The Kolmogorov-Smirnov test was used to determine the normal distribution of independent continuous variables (P > 0.05). A one-way analysis of variance (ANOVA) test was used to analyze continuous variables and a Chi-square test was used to compare qualitative variables www.nature.com/scientificreports/ according to tertiles of DDRRS. The analysis of covariance (ANCOVA) test was used to adjust the analysis for confounders and covariates including age, BMI, physical activity, and energy intake. Post-hoc (Bonferroni) analyses were performed to analyse the mean differences in continuous variables across tertiles of DDRRs. Linear regression analysis was used to examine associations between DDRRs and LAP, VAI, SMM, and other body composition components in the crude and adjusted models. The analysis was adjusted for potential confounders including age, energy intake, and physical activity in the first model and further for marital status and economic status in the second model. Findings were reported as Beta (β), standard error (SE), and 95% confidence intervals (CIs).
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After adjustment for energy intake, participants with the highest tertile of DDRR score had a higher intake of whole grain, vegetables, nuts, legumes, tea and coffee (P < 0.001) and a lower intake of SSB (P < 0.001), compared to those in the lowest tertile. Table 1. General characteristics according to tertiles of DDRRS in overweight and obese women (n = 291). AC arm circumference, ALT alanine transaminase, AMC arm muscle circumference, AST aspartate aminotransferase, BMC bone mineral content, BMI body mass index, DBP diastolic blood pressure, DDRRs dietary diabetes risk reduction score, EBW extracellular body water, FBS fasting blood sugar, HDL-C highdensity lipoprotein cholesterol, HOMA-IR hemostatic model assessment for insulin resistance, HC hipcircumference, IBW intracellular body water, QIUKI quantitative insulin sensitivity check index, LDL-C lowdensity lipoprotein cholesterol, SBP systolic blood pressure, SGOT serum glutamic-oxaloacetic transaminase, SGPT serum glutamic-pyruvic transaminase, PA physical activity, TBW total body water, TC total cholesterol, TG triglyceride, WC waist circumference. *P-value resulted from ANOVA analysis. **P-value reported from ANCOVA, after adjustment for age, energy intake, physical-activity, and BMI. BMI was considered as colinear variable. ***P value resulted from Chi-square test analysis. BMI was considered as collinear variable for anthropometric measurements and body composition. P-value < 0.05 was considered significant, and 0.05, 0.06, and 0.07 were considered marginally significant. a Significant difference was observed between T1 and T2. b Significant difference was observed between T1 and T3. c Significant difference was observed between T2 and T3. Significant and marginally significant values are in bold.  Table 3. While no significant mean difference in the crude model was observed, after controlling for confounders including age, energy intake, physical activity, marriage, and economic status, significant mean differences for VAI (P = 0.016) and LAP (P = 0.041) across tertiles of DDRRs were found. The results from Bonferroni posthoc test showed that the mean of VAI and LAP was higher in the first tertile compared to the second tertile. Table 2. Intake of macronutrients, micro-nutrients, and food groups according to tertiles of DDRRS in overweight and obese women (n = 291). EPA eicosapentaenoic acid, DHA docosahexaenoic acid, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid, SFA saturated fatty acid, SSB sugar-sweetened beverages, TFA trans fatty acid. *P-value resulted from ANOVA analysis. **P-value reported from ANCOVA after adjustment for energy intake. P-value < 0.05 was considered significant, and 0.05, 0.06, and 0.07 were considered marginally significant. Significant and marginally significant values are in bold.  Table 4. In the crude model, a significant positive association between DDRRs and VAI in tertile 2 (β: 0.96, 95% CI: 0.08, 1.83, P = 0.031) and a marginal inverse association between DDRRs and SLM in tertile 3 (β: − 1.49, 95% CI: − 2.99, 0.01, P = 0.053) was found. However, the significant association disappeared after adjustment for confounders (age, energy intake, physical activity, marital status, and economic status) in model 2. There was no significant association between DDRRs and LAP, trunk fat, BFM, FFM, SMM, BF%, WHR, VFA, VFL, FFMI, and FMI in the crude model (P > 0.05). However, after controlling for potential confounders in model 2, a negative association was found between DDRRs and trunk fat (P-value = 0.024), BFM (P-value = 0.023), BF% (P-value = 0.045), VFA (P-value = 0.0.26), and FMI (P-value = 0.045). There was no significant association between DDRRs and LAP, FFM, SMM, WHR, and VFL (P > 0.05). Furthermore, VAI (P trend = 0.052) and LAP (P trend = 0.069), TF (kg) (P trend = 0.27), TF% (%) (P trend = 0.074), BFM (P trend = 0.026), WHR (P trend = 0.066), VFA (P trend = 0.026), VFL (P trend = 0.064), FMI (P trend = 0.048) decreased with increasing tertiles of DDRRs (Table 4).

Discussion
According to our knowledge, this study is the first study investigated associations between DDRRs and LAP, VAL and SMM in overweight and obese women. According to our findings, there is an inverse and significant association between DDRRs and components of glycemic profiles (insulin, HOMA-IR), lipid profiles (TG), liver function enzymes (ALT, AST), and body composition indices (TF, BFM, FMI, BF%, VFA). Furthermore, body composition indices including VAI, LAP, TF, BFM, WHR, VFA, VFL, and FMI decreased significantly over DDRRs tertiles. However, no significant association was observed between VAI, LAP, and SMM and DDRRs. www.nature.com/scientificreports/ The findings of this study showed a significant inverse association between DDRRs and BFM. In accordance with the results of our study, Perry et al. revealed that higher adherence to the DASH-style diet is associated with lower body fat in obese older American adults. The DASH diet was characterized by a higher intake of nuts, whole grains, fruits, vegetables, and legumes and a lower intake of carbonated beverages and red meat that is comparable to the components of DDRRs in this study 30 .
Our findings showed that the higher DDRRs is associated with a lower level of lipid profiles (serum triglycerides (TGs)), insulin profiles (insulin level and homeostasis model assessment-insulin resistance (HOMA_IR)), liver enzymes (aspartate aminotransferase (AST) and alanine transaminase (ALT)). In line with our findings, previous studies reported that higher adherence to the DASH diet was associated with improved lipid profiles, reduced TG and liver enzymes, and improved glycemic profiles, reduced serum insulin levels and HOMA-IR score 31 . The existing evidence showed that the Mediterranean diet characterized by a higher intake of healthy food groups including whole grains, MUFA, plant proteins, seafood, fruits, and vegetables, significantly reduced the BFM, which was consistent with the results of our study 32,33 . Furthermore, in agreement with the findings of this study, the evidence showed that the Mediterranean dietary pattern reduced weight, BMI, WC, fasting insulin levels, HOMA-IR, fatty liver indexes, TG, fasting plasma glucose, AST, and ALT 34,35 . In addition, in the direction confirming the results of our study, previous studies showed that participants in the lower tertiles compared to those in the higher tertiles of DDRRs, had higher HOMA-IR, triglycerides, and alanine transaminase as well as greater adiposity levels that could be due to higher intake of refined grains, sugary drinks, and saturated and trans-fat and lower intake of whole grains and PUFA 36,37 .
The higher intake of coffee, nuts, fibre, and PUFAs as components of DDRRs has been individually associated with lower BFM, lipid profiles, glycemic profiles, and liver enzymes. A recent study has reported that daily coffee consumption was inversely associated with BMI, BF% 38 , VFA 39 , total abdominal fat 39 , insulin and insulin resistance 40 , and levels of ALT and AST 41 . These associations could be explained through various mechanisms. Coffee comprises various components with pharmacologic effects, including caffeine and chlorogenic acid (CGA) 38 . Previous evidence revealed that CGA consumption increased postprandial energy expenditure and fat utilization in healthy participants and showed a suppressing effect on the accumulation of body fat 39,42,43 . There is also a possible explanation that antioxidants in coffee could improve insulin sensitivity and inhibit the induction of liver enzymes 40,44,45 . Furthermore, caffeine, an important chemical component of coffee, can reduce the risk of Type-2 diabetes and serum triglyceride levels 46,47 . However, the existing evidence regarding the effect of coffee is mixed. In a study conducted with a larger sample size of both genders in Greek adults, regular coffee consumption was negatively associated with VAL and LAP levels 48 . A systematic review suggests that adding nuts to habitual diets tends to lower body weight, FM and improve insulin sensitivity 46,47 . This effect might be explained by the fact that nuts comprise magnesium, linolenic acid, L-arginine, antioxidants, and MUFA may function against inflammation and insulin resistance 47 . Also, nuts are high-fibre, protein, and low-glycemic food groups, that cause weight loss through increasing satiety 49 . However, the evidence of the effect of nuts is inconsistent. A recent meta-analysis of randomized controlled trials demonstrated a diet with a higher intake of nuts had no significant impact on adiposity-related measurements compared to the control group 50 .
This study revealed that higher DDRRs is associated with a lower level of lipid profiles, insulin profiles and liver enzymes. A possible explanation may be that high fibre intake which is one of the components of DDRRs reduces body fat distribution 51 , lipid profiles 51,52 , fasting insulin, HOMA-IR score 53 , and liver function 54 . Furthermore, the low energy density of insoluble dietary fibre can improve postprandial satiety, lead to weight loss, and improves liver enzymes 55,56 . On the other hand, soluble fibre can reduce insulin resistance and inflammation 53 .
Opposite to our findings, which showed no significant association between DDRRs and VAL and LAP, Mazidi et al. reported that higher fibre intake in a healthy dietary pattern is associated with lower levels of VAL and LAP. The conflict results may be due to including a large number of participants from both genders in this study compared to our study, which included only women 57 .
As mentioned, this study showed that higher DDRRs is associated with lower lipid profiles, insulin profiles, and liver enzymes, which may be related to PUFAs as one of the components of DDRRs. Recent studies demonstrated that a high ratio of PUFA/SFA is associated with lower body fatness 58 , insulin resistance 59 , lipid metabolism 60 , and hepatic enzyme parameters 61 . Furthermore, it has been suggested that n-3 PUFAs may activate a metabolic change in adipocytes including increased β-oxidation, lipogenesis suppression in abdominal fat 62 , and inducing apoptosis in the adipose tissue (AT) 63 . Also, n-3 PUFAs activate the peroxisome proliferatoractivated receptor (PPAR) alpha, which in turn stimulates fatty acid oxidation 64 , and PPAR gamma increases insulin sensitivity 65 , inhibits hepatic lipogenesis, and reduces hepatic reactive oxygen species 66 . While a randomized controlled trial study in 2021 showed that omega-3 (n-3 PUFAs) supplementation improved LAP and VAI levels, this study found no significant association which might be due to the fact that our study design was cross-sectional, while their study was a randomized controlled trial on diabetic patients with nonalcoholic fatty liver disease (NAFLD) 67 . Finally, it is likely that the anti-inflammatory, anti-atherogenic, decreasing visceral adiposity and improving dyslipidemia and hyperinsulinemia effects of DDRRs is due to its components, including antioxidants, vitamins and minerals, phenolic compounds, and unsaturated fatty acid 17,23 .
The current study has several limitations that should be considered in interpreting the results. Firstly, due to the cross-sectional design, causality cannot be conferred. As a result, further prospective observational studies and randomized clinical trials are needed to confirm the effect of DDRRs on LAP, VAL, and SMM. Secondly, using FFQs can result in under or over-reporting dietary intake. Thirdly, this study included only women; thus, it is impossible to generalize the results to the whole population. Lastly, using the categorical confounders might result in residual confounding. This study also has several strengths. This study is the first to show the link between DDRRs and LAP, VAL and SMM in adult women. This study included a large sample size and the analysis was controlled for various potential confounders.